Abstract

A new non-invasive method for delineation of lentigo
maligna and lentigo maligna melanoma is demonstrated. The
method is based on the analysis of the hyperspectral
images taken in vivo before surgical excision of the
lesions. For this, the characteristic features of the
spectral signatures of diseased pixels and healthy pixels
are extracted, which combine the intensities in a few
selected wavebands with the coefficients of the wavelet
frame transforms of the spectral curves. To reduce
dimensionality and to reveal the internal structure of
the datasets, the diffusion maps technique is applied.
The averaged Nearest Neighbor and the Classification and
Regression Tree (CART) classifiers are utilized as the
decision units. To reduce false alarms by the CART
classifier, the Aisles procedure is used.

abstract = "A new non-invasive method for delineation of lentigo maligna and lentigo maligna melanoma is demonstrated. The method is based on the analysis of the hyperspectral images taken in vivo before surgical excision of the lesions. For this, the characteristic features of the spectral signatures of diseased pixels and healthy pixels are extracted, which combine the intensities in a few selected wavebands with the coefficients of the wavelet frame transforms of the spectral curves. To reduce dimensionality and to reveal the internal structure of the datasets, the diffusion maps technique is applied. The averaged Nearest Neighbor and the Classification and Regression Tree (CART) classifiers are utilized as the decision units. To reduce false alarms by the CART classifier, the Aisles procedure is used.",

N2 - A new non-invasive method for delineation of lentigo
maligna and lentigo maligna melanoma is demonstrated. The
method is based on the analysis of the hyperspectral
images taken in vivo before surgical excision of the
lesions. For this, the characteristic features of the
spectral signatures of diseased pixels and healthy pixels
are extracted, which combine the intensities in a few
selected wavebands with the coefficients of the wavelet
frame transforms of the spectral curves. To reduce
dimensionality and to reveal the internal structure of
the datasets, the diffusion maps technique is applied.
The averaged Nearest Neighbor and the Classification and
Regression Tree (CART) classifiers are utilized as the
decision units. To reduce false alarms by the CART
classifier, the Aisles procedure is used.

AB - A new non-invasive method for delineation of lentigo
maligna and lentigo maligna melanoma is demonstrated. The
method is based on the analysis of the hyperspectral
images taken in vivo before surgical excision of the
lesions. For this, the characteristic features of the
spectral signatures of diseased pixels and healthy pixels
are extracted, which combine the intensities in a few
selected wavebands with the coefficients of the wavelet
frame transforms of the spectral curves. To reduce
dimensionality and to reveal the internal structure of
the datasets, the diffusion maps technique is applied.
The averaged Nearest Neighbor and the Classification and
Regression Tree (CART) classifiers are utilized as the
decision units. To reduce false alarms by the CART
classifier, the Aisles procedure is used.